The present invention relates to a noise canceler and, more particularly, to a noise canceler for canceling, by use of an adaptive filter, a background noise signal introduced into a speech signal input via a microphone, a handset or the like.
A background noise signal introduced into a speech signal input via, e g., a microphone or a handset is a critical problem when it comes to a narrow band speech coder, speech recognition device and so forth which compress information to a high degree. Noise cancelers for canceling such acoustically superposed noise components include a biinput noise canceler using an adaptive filter and taught in B. Widrow et al. "Adaptive Noise Cancelling: Principles and Applications", PROCEEDINGS OF IEEE, VOL. 63, NO. 12, DECEMBER 1975, pp. 1692-1716 (Document 1 hereinafter).
The noise canceler taught in Document 1 includes an adaptive filter for approximating the impulse response of a noise path along which a noise signal input to a microphone assigned to a reference signal (reference signal microphone hereinafter) to propagate toward a microphone assigned to a main signal (main signal microphone hereinafter). The adaptive filter is capable estimating noise introduced into the main signal microphone. The estimated noise signal is subtracted from a main signal (combination of a desired signal and a noise signal) input to the main signal microphone.
The filter coefficient of the above adaptive filter is corrected by determining a correlation between an error signal produced by subtracting the estimated noise signal from the main signal and a reference signal derived from the reference signal microphone. Typical of an algorithm for such coefficient correction, i.e., a convergence algorithm is "LMS algorithm" describe in Document 1 or "LIM (Learning Identification Method) algorithm" described in EEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 12, NO. 3, 1967, pp. 282-287.
A conventional noise cancellation principle will be described with reference to FIG. 5. As shown, a noise canceler includes a main signal microphone 1, a reference signal microphone 2, an adaptive filter 3, a subtracter 4, and an output terminal 5. A desired signal S(z) spoken by a speaker (signal source) is input to the main signal microphone 1 adjoining the speaker's mouth by way of a path having an acoustic transfer characteristic HA(z); z is expressed as: EQU z=exp(2.alpha.j/FS) Eq. (1)
where FG denotes a sampling frequency.
On the other hand, noise N(z) issuing from a noise source is input to the main signal microphone 1 via a path having an acoustic transfer characteristic GA(z). At the same time, the noise N(z) is input to a reference signal microphone 2 remote from the speaker by way of a path having an acoustic transfer characteristic GB(z). The adaptive filter 3 estimates, based on the main signal XA(z) and reference signal XB(z), the acoustic transfer characteristic (noise path) P(z) of an acoustic path along which noise output from the noise source N(z) and then input to the reference signal microphone 2 will propagate to the main signal microphone 1 when the desired signal S(z) is not input.
The acoustic transfer characteristic P(z) to be estimated is produced by: EQU P(z)=GA(z)/GB(z) Eq. (2)
The adaptive filter 3 therefore constitutes a filter having a transfer characteristic W1(z) identical with the transfer function P(z) and operates to generate an estimated noise signal F1(z) identical with the noise signal contained in the main signal. The subtracter 4 subtracts the estimated noise signal F1(z) output from the filter 3 from the main signal XA(z), thereby producing an output E1(z). When the desired signal S(z) is not input, the output signal E1(z) is expressed as: ##EQU1##
In this manner, the adaptive filter 3 is capable of estimating the acoustic transfer characteristic P(z) by updating the coefficient such that the output signal E1(z) is zero when the desired signal S(z) is not contained. The output signal E1(z) is referred to as an error signal because it is representative of an error in the learning operation of the adaptive filter.
After the convergence of the adaptive filter 3 the output signal E1(z) is expressed as: ##EQU2##
As the Eq. (4) indicates, the output signal E1(z) does not contain any noise signal N(z), i.e., noise has been canceled. However, the problem is that when the reference signal microphone 2 contains the desired signal component S(z), i.e., when the acoustic transfer characteristic HB(z) from the desired signal S(z) to the reference signal microphone 2 is not zero, a signal distortion represented by [1-{HB(z)/HA(z)}W1(z)] occurs.
To solve the above problem, an adaptive filter for correcting the signal distortion contained in the output signal S1(z) may be added, as taught in Japanese Patent Laid-Open Publication No. 8-56180. FIG. 6 shows a noise canceler including such an additional adaptive filter. As shown, the noise canceler has an adaptive filter 6 for the above correction and a subtracter 7 in addition to the structural elements shown in FIG. 5. When the main signal XA(z) contains the desired signal S(z) and if noise is absent is of less than certain level, the adaptive filter 6 performs learning such that the output E2(x) of the substracter 7 decreases. Assuming that the adaptive filter 6 has a transfer characteristic W2(z), then the filter 6 performs the above learning based on, e.g., the LIM scheme such that when N(z) is zero or negligible,
E2(z) has the following value: ##EQU3##
Therefore, the transfer characteristic W2(z) of the adaptive filter 6 is produced by: ##EQU4##
The output F2(z) of the adaptive filter 6 derived from the learning is expressed as: ##EQU5## As a result, a desired signal HA(z)S(z) free from signal distortion is output.
As stated above, the conventional noise canceler updates the coefficient of the adaptive filter 3 and learns the acoustic characteristic of noise in sections where the noise signal N(z) is present and the desired signal component S(z) is absent or negligibly small. Further, the noise canceler updates the coefficient of the adaptive filter 4 and learns a signal distortion correction filter in sections where the desired signal component S(z) is present and the noise component N(z) is absent or negligibly small. It is therefore necessary to detect the above sections where the desired signal component S(z) is absent (or little) and the sections where the noise signal component N(z) is absent (or little) and to command the adaptive filters to perform leaning in such sections from the outside.
However, it is, in many cases, difficult to command the adaptive filters to perform learning from the outside in accordance with the level of the desired signal and that of the noise signal, depending on the situation in which the noise canceler is located. With the conventional noise canceler, a sufficient noise canceling ability and a sufficient distortion correction characteristic are not achievable unless adequate learning sections are indicated to each adaptive filter for the learning purpose.